Quantitative Aerosol Optical Depth Detection during Dust Outbreaks from Meteosat Imagery Using an Artificial Neural Network Model
This study presents the development of an artificial neural network (ANN) model to quantitatively estimate the atmospheric aerosol load (in terms of aerosol optical depth, AOD), with an emphasis on dust, over the Mediterranean basin using images from Meteosat satellites as initial information. More...
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Online Access: | https://doi.org/10.3390/rs11091022 https://doaj.org/article/d93d0f7f88e44bdb9e76b9905f96aa0e |
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ftdoajarticles:oai:doaj.org/article:d93d0f7f88e44bdb9e76b9905f96aa0e 2023-05-15T13:06:08+02:00 Quantitative Aerosol Optical Depth Detection during Dust Outbreaks from Meteosat Imagery Using an Artificial Neural Network Model Stavros Kolios Nikos Hatzianastassiou 2019-04-01T00:00:00Z https://doi.org/10.3390/rs11091022 https://doaj.org/article/d93d0f7f88e44bdb9e76b9905f96aa0e EN eng MDPI AG https://www.mdpi.com/2072-4292/11/9/1022 https://doaj.org/toc/2072-4292 2072-4292 doi:10.3390/rs11091022 https://doaj.org/article/d93d0f7f88e44bdb9e76b9905f96aa0e Remote Sensing, Vol 11, Iss 9, p 1022 (2019) Dust detection Meteosat satellite remote sensing artificial neural networks Mediterranean AERONET Science Q article 2019 ftdoajarticles https://doi.org/10.3390/rs11091022 2022-12-31T16:09:56Z This study presents the development of an artificial neural network (ANN) model to quantitatively estimate the atmospheric aerosol load (in terms of aerosol optical depth, AOD), with an emphasis on dust, over the Mediterranean basin using images from Meteosat satellites as initial information. More specifically, a back-propagation ANN model scheme was developed to estimate visible (at 550 nm) aerosol optical depth (AOD 550 nm ) values at equal temporal (15 min) and spatial (4 km) resolutions with Meteosat imagery. Accuracy of the ANN model was thoroughly tested by comparing model estimations with ground-based AOD 550 nm measurements from 14 AERONET (Aerosol Robotic NETwork) stations over the Mediterranean for 34 selected days in which significant dust loads were recorded over the Mediterranean basin. Using a testbed of 3076 pairs of modeled and measured AOD 550 nm values, a Pearson correlation coefficient (r P ) equal to 0.91 and a mean absolute error (MAE) of 0.031 were found, proving the satisfactory accuracy of the developed model for estimating AOD 550 nm values. Article in Journal/Newspaper Aerosol Robotic Network Directory of Open Access Journals: DOAJ Articles Remote Sensing 11 9 1022 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Dust detection Meteosat satellite remote sensing artificial neural networks Mediterranean AERONET Science Q |
spellingShingle |
Dust detection Meteosat satellite remote sensing artificial neural networks Mediterranean AERONET Science Q Stavros Kolios Nikos Hatzianastassiou Quantitative Aerosol Optical Depth Detection during Dust Outbreaks from Meteosat Imagery Using an Artificial Neural Network Model |
topic_facet |
Dust detection Meteosat satellite remote sensing artificial neural networks Mediterranean AERONET Science Q |
description |
This study presents the development of an artificial neural network (ANN) model to quantitatively estimate the atmospheric aerosol load (in terms of aerosol optical depth, AOD), with an emphasis on dust, over the Mediterranean basin using images from Meteosat satellites as initial information. More specifically, a back-propagation ANN model scheme was developed to estimate visible (at 550 nm) aerosol optical depth (AOD 550 nm ) values at equal temporal (15 min) and spatial (4 km) resolutions with Meteosat imagery. Accuracy of the ANN model was thoroughly tested by comparing model estimations with ground-based AOD 550 nm measurements from 14 AERONET (Aerosol Robotic NETwork) stations over the Mediterranean for 34 selected days in which significant dust loads were recorded over the Mediterranean basin. Using a testbed of 3076 pairs of modeled and measured AOD 550 nm values, a Pearson correlation coefficient (r P ) equal to 0.91 and a mean absolute error (MAE) of 0.031 were found, proving the satisfactory accuracy of the developed model for estimating AOD 550 nm values. |
format |
Article in Journal/Newspaper |
author |
Stavros Kolios Nikos Hatzianastassiou |
author_facet |
Stavros Kolios Nikos Hatzianastassiou |
author_sort |
Stavros Kolios |
title |
Quantitative Aerosol Optical Depth Detection during Dust Outbreaks from Meteosat Imagery Using an Artificial Neural Network Model |
title_short |
Quantitative Aerosol Optical Depth Detection during Dust Outbreaks from Meteosat Imagery Using an Artificial Neural Network Model |
title_full |
Quantitative Aerosol Optical Depth Detection during Dust Outbreaks from Meteosat Imagery Using an Artificial Neural Network Model |
title_fullStr |
Quantitative Aerosol Optical Depth Detection during Dust Outbreaks from Meteosat Imagery Using an Artificial Neural Network Model |
title_full_unstemmed |
Quantitative Aerosol Optical Depth Detection during Dust Outbreaks from Meteosat Imagery Using an Artificial Neural Network Model |
title_sort |
quantitative aerosol optical depth detection during dust outbreaks from meteosat imagery using an artificial neural network model |
publisher |
MDPI AG |
publishDate |
2019 |
url |
https://doi.org/10.3390/rs11091022 https://doaj.org/article/d93d0f7f88e44bdb9e76b9905f96aa0e |
genre |
Aerosol Robotic Network |
genre_facet |
Aerosol Robotic Network |
op_source |
Remote Sensing, Vol 11, Iss 9, p 1022 (2019) |
op_relation |
https://www.mdpi.com/2072-4292/11/9/1022 https://doaj.org/toc/2072-4292 2072-4292 doi:10.3390/rs11091022 https://doaj.org/article/d93d0f7f88e44bdb9e76b9905f96aa0e |
op_doi |
https://doi.org/10.3390/rs11091022 |
container_title |
Remote Sensing |
container_volume |
11 |
container_issue |
9 |
container_start_page |
1022 |
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1766404406428827648 |